package ds_industry_2025.ds.ds_02.T2

import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.QuickstartUtils.getQuickstartWriteConfigs
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._

import java.text.SimpleDateFormat
import java.util.{Calendar, Properties}

/*
 3、抽取ods_ds_hudi库base_province表中昨天的分区（子任务一生成的分区）数据，并结合dim_province最新分区现有的数据，根据id合并
 数据到dwd_ds_hudi库中dim_province的分区表（合并是指对dwd层数据进行插入或修改，需修改的数据以id为合并字段，根据create_time排
 序取最新的一条），分区字段为etl_date且值与ods_ds_hudi库的相对应表该值相等，并添加dwd_insert_user、dwd_insert_time、
 dwd_modify_user、dwd_modify_time四列,其中dwd_insert_user、dwd_modify_user均填写“user1”。若该条数据第一次进入数仓dwd层
 则dwd_insert_time、dwd_modify_time均填写当前操作时间，并进行数据类型转换。若该数据在进入dwd层时发生了合并修改，
 则dwd_insert_time时间不变，dwd_modify_time存当前操作时间，其余列存最新的值。id作为primaryKey，dwd_modify_time作
 preCombineField。使用spark-shell在表dwd.dim_province最新分区中，查询该分区中数据的条数，将结果截图粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下；
 */
object t3 {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local[*]")
      .appName("t1")
      .config("hive.exec.dynamic.partition.mode", "nonstrict")
      .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
      .config("spark.sql.extensions", "org.apache.spark.sql.hudi.HoodieSparkSessionExtension")
      .enableHiveSupport()
      .getOrCreate()

    val conn = new Properties()
    conn.setProperty("user", "root")
    conn.setProperty("password", "123456")
    conn.setProperty("driver", "com.mysql.jdbc.Driver")

    val day = Calendar.getInstance()
    val current_time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(day.getTime)
    day.add(Calendar.DATE, -1)
    val yesterday = new SimpleDateFormat("yyyyMMdd").format(day.getTime)

    val ods_path = "hdfs://192.168.40.110:9000/user/hive/warehouse/ods_ds_hudi.db/base_province"
    val dwd_path="hdfs://192.168.40.110:9000/user/hive/warehouse/dwd_ds_hudi.db/dim_province"

    val ods = spark.read.format("hudi").load(ods_path)
      .drop("etl_date")
      .withColumn("dwd_insert_user", lit("user1"))
      .withColumn("dwd_insert_time", to_timestamp(lit(current_time)))
      .withColumn("dwd_modify_user", lit("user1"))
      .withColumn("dwd_modify_time", to_timestamp(lit(current_time)))


    spark.read.format("hudi").load(dwd_path)
      .createOrReplaceTempView("t1")

    val dwd = spark.read.format("hudi").load(dwd_path)
      .where("etl_date=(select max(etl_date) from t1)")
      .drop("etl_date")
      .withColumn("dwd_modify_time", to_timestamp(lit(current_time)))
    
    ods.unionAll(dwd)
      .withColumn(
        "dwd_insert_time",
        min("dwd_insert_time").over(Window.partitionBy("id"))
      )
      .withColumn(
        "row",
        row_number().over(Window.partitionBy("id").orderBy(desc("create_time")))
        )
      .where(col("row")===1)
      .drop("row")
      .withColumn("etl_date", lit(yesterday))
      .write.format("hudi").mode("append")
      .options(getQuickstartWriteConfigs)
      .option(RECORDKEY_FIELD.key(), "id")
      .option(PRECOMBINE_FIELD.key(), "dwd_modify_time")
      .option(PARTITIONPATH_FIELD.key(), "etl_date")
      .option("hoodie.table.name", "dim_province")
      .save(dwd_path)


    //  spark.sql("select count(*) from dwd_ds_hudi.dim_province where etl_date=(select max(etl_date) from dwd_ds_hudi.dim_province)").show

    spark.close()
  }

}
